MutationForecaster® (mutationforecaster.com) is Cytognomix’s patented web-portal for analysis of all types of mutations (coding and non-coding), including interpretation, comparison and management of genetic variant data. It’s a fully automated genome interpretation solution for research, translational and clinical labs.
MutationForecaster® combines our world-leading genome interpretation software on your exome, gene panel, or complete genome (Shannon transcription factor and splicing pipelines, ASSEDA, Veridical) with the Cytognomix User Variation Database and Variant Effect Predictor. With our integrated suite of software products, analyze coding, non-coding, and copy number variants, and compare new results with existing or your own database. Select predicted mutations by phenotype using articles with CytoVisualization Analytics. With Workflows, automatically perform end-to-end analysis with all of our software products.
Download an 1 page overview of MutationForecaster®: link .
You can now experience our integrated suite of genome interpretation products through a free trial of MutationForecaster®. Once you register, analyze datasets that we have analyzed in our peer-reviewed publications with any of our software tools.
Ionizing radiation produces characteristic chromosome changes. The altered chromosomes contain two central constrictions, termed centromeres, instead of one (known as dicentric chromosomes [DCs]). Chromosome biodosimetry is approved by the IAEA for occupational radiation exposure, radiation emergencies, or monitoring long term exposures. In emergency responses to a range of doses, labs need efficient methods that identify DCs.
Cytognomix has developed a novel approach to find DCs that is independent of chromosome length, shape and structure from different laboratories (paper: TBME). The Automated Dicentric Chromosome Identifier and Dose Estimator (ADCI) software works on multiple platforms and uses images produced by any of the existing automated metaphase capture systems found in most cytogenetic laboratories. ADCI is now available for for trial or purchase (link). Or contact us for details (pricing).
ADCI* uses machine learning based algorithms with high sensitivity and specificity that distinguish monocentric and dicentric chromosomes (Try the Dicentric Chromosome Identifier web app). With novel image segmentation, ADCI has become a fully functional cytogenetic biodosimetry system. ADCI takes images from all types of commercial metaphase scanning systems, selects high quality cells for analysis, identifies dicentric chromosomes (removing false positives), builds biodosimetry calibration curves, and estimates exposures. ADCI fulfills the criteria established by the IAEA for accurate triage biodosimetry of a sample in less than an hour. The accuracy is comparable to an experienced cytogeneticist. Check out our online user manual: wiki.
We find and validate mutations that others cannot with advanced, patented genomic probe and bioinformatic technologies. Cytognomix continues our long track record of creating technologies for genomic medicine. We anticipate and implement the needs of the biomedical and clinical genomics communities.
Browse the products section of the menu found in the header bar for more information regarding any of our services.
- Don’t want to run your own analyses on MutationForecaster®? Let us do it for you with our Bespoke Analysis Service.
- Customized genomic microarrays
- Ultrahigh resolution FISH probes:
- Microarray-based comparative genomic hybridization (aCGH) can use SC technology to increase reproducibility and reduce cost per sample.
CytoGnomix’s Mutation Forecaster: Key to discovery of mutations in novel ALS gene. Nicolas et al. Genome-wide Analyses Identify KIF5A as a Novel ALS Gene. Neuron 97:1268-1283.e6, 2018 http://www.cell.com/neuron/fulltext/S0896-6273(18)30148-X From our subscriber, Dr. John Landers (U. Mass. School of Medicine): “We used the application ASSEDA (Automated Splice Site and Exon Definition Analyses) to predict any mutant […]
April 9, 2018. Upcoming release of Automated Dicentric Chromosome Identifier and Dose Estimator (ADCI)
We have just completed porting Windows ADCI from the MinGW C++ (32 bit) to Microsoft’s C++ (64 bit) compiled version. The release of this software in summer 2018 will contain this new version (v 2.0). ADCI now has access to 8 Gb of runtime memory, which should allow twice as many samples to be batch […]
Clustered, information-dense transcription factor binding sites identify genes with similar tissue-wide expression profiles. BioRxiv, 2018. doi: https://doi.org/10.1101/283267
On behalf of the Scientific Programme Committee of the European Conference of Human Genetics 2018 taking place in Milan, Italy from June 16 to June 19, 2018, we are pleased to inform you that the abstract entitled: ‘Comprehensive prediction of responses to chemotherapies by biochemically-inspired machine learning’ (Control No. 2018-A-2095-ESHG) was among the best scored […]
We have published a new approach to devise gene signatures to detect radiation exposure (human, murine), and to quantify levels of exposure (murine): Zhao JZL, Mucaki EJ and Rogan PK. Predicting ionizing radiation exposure using biochemically-inspired genomic machine learning. F1000Research 2018, 7:233 (doi: 10.12688/f1000research.14048.1)
Manuscript describing accurate genomic signatures of radiation exposure will be published shortly by F1000Research. Jonathan ZL Zhao, Eliseos J Mucaki, Peter K Rogan. Predicting Exposure to Ionizing Radiation by Biochemically-Inspired Genomic Machine Learning, F1000Research, in press. Abstract: Background: Gene signatures derived from transcriptomic data using machine learning methods have shown promise for biodosimetry testing. These […]
Ali, S, Li Y, Shirley B, Wilkins R, Flegal F, Rogan PK, Knoll JHM. Population scale biodosimetry with the Automated Dicentric Chromosome Identifier and Dose Estimator (ADCI) software system. [Platform] Rogan PK, Zhao JZL, and Mucaki EJ. Predicting exposure to ionizing radiation by biochemically-inspired genomic machine learning.[Poster] Li Y, Shirley B, Wilkins R, Flegal, F, […]
May 4, 2015. Comment on PMID 23348723. Prediction of mutant mRNA splice isoforms by information theory-based exon definition.
Peter Rogan 2015 May 04 6:14 p.m. The Logic and Formulation of Exon Definition for Splice and Splicing Regulatory Sites with Negative Information Content. PK Rogan, EJ Mucaki Update on: Mucaki EJ, 2013 and the Automated Splice Site and Exon Definition Analysis server (ASSEDA). In Mucaki EJ, 2013, we described a method of predicting the overall strength of an exon […]